Can Qwen 3.5 9B run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Qwen 3.5 9B needs ~13.7 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~126 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.7 GB, 126.0 tok/s, Runs well

13.7 GB required48.0 GB available

29% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

131K

Memory

13.7 GB / 48.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on RTX PRO 5000 Blackwell 48GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	126.0 tok/s	838 ms	131K
Coding	S	Runs well	126.0 tok/s	1537 ms	131K
Agentic Coding	S	Runs well	126.0 tok/s	2235 ms	131K
Reasoning	S	Runs well	126.0 tok/s	1816 ms	131K
RAG	S	Runs well	126.0 tok/s	2794 ms	131K

Inference speed

Qwen 3.5 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	119.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	90.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	86.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	73.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	73.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	71.5	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.1	Fits
RTX 3060 12GB	12 GB	Q4_K_M	39.9	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	37.9	Fits
RTX 4060 8GB	8 GB	Q4_K_M	19.2	Heavy offload

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A83
Q3_K_S	3	4.4 GB	Low	A83
NVFP4	4	5.0 GB	Medium	A83
Q4_K_M	4	5.5 GB	Medium	A83
Q5_K_M	5	6.5 GB	High	A84
Q6_K	6	7.4 GB	High	A84
Q8_0	8	9.6 GB	Very High	A84
F16Best for your GPU	16	18.5 GB	Maximum	S87

Get started

Copy-paste commands to run Qwen 3.5 9B on your machine.

Run

ollama run qwen3.5:9b

Your hardware

More models your RTX PRO 5000 Blackwell 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	170.7 tok/s
Qwen 3.5 27B	27B	S	74 tok/s
Qwen 3.6 27B	27B	S	74.3 tok/s
Qwen 3.6 35B A3B	35B	S	143.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	176.6 tok/s

Frequently asked questions

Can RTX PRO 5000 Blackwell 48GB run Qwen 3.5 9B?

Yes, RTX PRO 5000 Blackwell 48GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 126.0 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 9B?

The recommended quantization for Qwen 3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 9B run at on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Qwen 3.5 9B achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on RTX PRO 5000 Blackwell 48GB receives a S grade with 126.0 tok/s and 131K context.

What context window can Qwen 3.5 9B use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Qwen 3.5 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for Qwen 3.5 9B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3.5-9b-on-rtx-pro-5000-blackwell-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

126.0 tok/s

838 ms

131K

Coding

Runs well

126.0 tok/s

1537 ms

131K

Agentic Coding

Runs well

126.0 tok/s

2235 ms

131K

Reasoning

Runs well

126.0 tok/s

1816 ms

131K

RAG

Runs well

126.0 tok/s

2794 ms

131K

Qwen 3.5 9B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

126.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

126.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

119.6

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

109.1

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

90.9

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

86.2

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

73.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

73.4

Fits

RTX 4070 12GB

12 GB

Q4_K_M

71.5

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

47.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

43.1

Fits

RTX 3060 12GB

12 GB

Q4_K_M

39.9

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

37.9

Fits

RTX 4060 8GB

8 GB

Q4_K_M

19.2

Heavy offload

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A83
Q3_K_S	3	4.4 GB	Low	A83
NVFP4	4	5.0 GB	Medium	A83
Q4_K_M	4	5.5 GB	Medium	A83
Q5_K_M	5	6.5 GB	High	A84
Q6_K	6	7.4 GB	High	A84
Q8_0	8	9.6 GB	Very High	A84
F16Best for your GPU	16	18.5 GB	Maximum	S87

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

170.7 tok/s

Qwen 3.5 27B

27B

74 tok/s

Qwen 3.6 27B

27B

74.3 tok/s

Qwen 3.6 35B A3B

35B

143.5 tok/s

Qwen3-VL 30B A3B Instruct

30B

176.6 tok/s

Frequently asked questions

Can RTX PRO 5000 Blackwell 48GB run Qwen 3.5 9B?

Yes, RTX PRO 5000 Blackwell 48GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 126.0 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 9B?

The recommended quantization for Qwen 3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 9B run at on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Qwen 3.5 9B achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on RTX PRO 5000 Blackwell 48GB receives a S grade with 126.0 tok/s and 131K context.

What context window can Qwen 3.5 9B use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Qwen 3.5 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.